The present description relates generally to systems and methods used to estimate credit risk pertaining to financial assets, such as loans, securities, and so forth. More particularly, the present description relates to a method and system for evaluating credit risk associated with a financial asset by determining the probability of an event in connection with the asset.
The financial services industry transacts billions of dollars every month. Such transactions typically include mortgage loans, auto loans, student loans, business loans, consumer credit cards, etc. As such, the ability to assess risks, such as credit risk, is important in the context of financial services. Credit risk refers to the possibility that a borrower will fail to pay a periodic debt obligation to a lender. For example, mortgage and auto loans may become delinquent for several months and lenders may eventually declare the loans in default. A defaulted loan or a delinquent loan is costly to the owner of the asset (initially the lender) when the default results in a loss on the loan.
Loan risk management requires forming expectations over the possible future payment outcomes such as delinquency levels, or final terminations as defaults or full repayments. As a lender improves its ability to determine risk associated with a loan, it can make better underwriting and pricing decisions that will result in fewer loans that become delinquent and/or end in a default resulting in a financial loss. In the secondary mortgage market, where mortgage loans are commonly sold to investors, fewer defaulted/delinquent loans results in lower losses and a better return on investment, resulting in increased capital flow to the housing market. Even for loans that have already been made to a borrower, better risk predictions allow more effective risk management strategies to be employed (e.g., more effective hedging or workout strategies) and, therefore, decrease vulnerability to losses due to defaults/delinquencies. Better risk predictions, therefore, decrease the defaults/delinquencies, improve capital flow to the housing market, and ultimately decrease mortgage costs for consumers.
A large number of factors may be used to assess risk associated with a loan, including borrower-specific risk factors, loan-specific risk factors, and property/collateral-specific risk factors. Borrower-specific risk factors may include factors such as the borrower's credit score as mentioned above, as well as other factors such as the borrower's income and financial reserves. Property-specific risk factors may include factors such as whether the property is owner-occupied. Loan-specific risk factors may include factors such as the loan-to-value ratio, the loan amount, the loan purpose, and so on.
In evaluating the credit risk associated with the loan, a lender or investor may use a mathematical model to form expectations over the possible future payment outcomes such as delinquency levels, defaults, etc. Determining these expectations in many applications may typically involve use of additional mathematical models in order to perform updated calculations or predictions of the transition over time of other parameters related to the probability of the adverse event, and may further involve simultaneously tracking several possible transition values of these parameters. For example, determining the probability of default and prepayment for a mortgage loan typically involves simultaneously tracking several potential delinquency states for the loan simultaneously, which are connected by a matrix of delinquency transition probabilities.
Calculating and tracking these various transition states may become computationally intensive and slow down the determination of the probability of the adverse event associated with the loan. Further, the pattern of transition states may provide little additional useful information in some business applications. Thus, there is a need for method and system for evaluating credit risk associated with a loan that is configured to determine the probability of an adverse event associated with a financial asset based on an initial observation of a parameter without requiring additional models to determine transition states for the parameter. There is further need for a method and system for evaluating credit risk associated with a loan that is configured to determine the probability of an adverse event associated with both seasoned and unseasoned mortgage loans.